Evaluation of undiagnosed solitary lung nodules according to the probability of malignancy in the American College of Chest Physicians (ACCP) evidence-based clinical practice guidelines

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

BACKGROUND

Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.

METHODS

We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.

RESULTS

A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.

CONCLUSIONS

The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020161559.

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND

The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT).

PATIENTS AND METHODS

Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic ¹⁸F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUV_max) was measured on conventional ¹⁸F-FDG PET/CT images. The influx constant (K_i ) was calculated from the dynamic ¹⁸F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test.

RESULTS

Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUV_max values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average K_i values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min^-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613).

CONCLUSIONS

Dynamic ¹⁸F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

Sensitivity and Safety of Electromagnetic Navigation Bronchoscopy for Lung Cancer Diagnosis: Systematic Review and Meta-analysis

BACKGROUND

Bronchoscopy is a useful tool for the diagnosis of lesions near central airways; however, the diagnostic accuracy of these procedures for peripheral pulmonary lesions (PPLs) is a matter of ongoing debate. In this setting, electromagnetic navigation bronchoscopy (ENB) is a technique used to navigate and obtain samples from these lesions. This systematic review and meta-analysis aims to explore the sensitivity of ENB in patients with PPLs suspected of lung cancer.

RESEARCH QUESTION

In patients with peripheral pulmonary lesion suspected of lung cancer, what is the sensitivity and safety of electromagnetic navigation bronchoscopy compared to surgery or longitudinal follow up?

STUDY DESIGN AND METHODS

A comprehensive search of several databases was performed. Extracted data included sensitivity of ENB for malignancy, adequacy of the tissue sample, and complications. The study quality was assessed using the QUADAS-2 tool, and the combined data were meta-analyzed using a bivariate method model. A summary receiver operatic characteristic curve (sROC) was created. Finally, the quality of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation approach.

RESULTS

Forty studies with a total of 3,342 participants were included in our analysis. ENB reported a pooled sensitivity of 77% (95% CI, 72%-82%; I² = 80.6%) and a specificity of 100% (95% CI, 99%-100%; I² = 0%) for malignancy. The sROC showed an area under the curve of 0.955 (P = .03). ENB achieved a sufficient sample for ancillary tests in 90.9% (95% CI, 84.8%-96.9%; I² = 80.7%). Risk of pneumothorax was 2.0% (95% CI, 1.0-3.0; I² = 45.2%). We found subgroup differences according to the risk of bias and the number of sampling techniques. Meta-regression showed an association between sensitivity and the mean distance of the sensor tip to the center of the nodule, the number of tissue sampling techniques, and the cancer prevalence in the study.

INTERPRETATION

ENB is very safe with good sensitivity for diagnosing malignancy in patients with PPLs. The applicability of our findings is limited because most studies were done with the superDimension navigation system and heterogeneity was high.

TRIAL REGISTRY

PROSPERO; No.: CRD42019109449; URL: https://www.crd.york.ac.uk/prospero/.

Evaluation of the solitary pulmonary nodule

The solitary pulmonary nodule represents a common diagnostic challenge for clinicians. While most are benign, a significant number represent early, potentially curable lung cancers. With the increased utilisation of chest computed tomography, solitary pulmonary nodules are increasingly being identified and with lung cancer screening programmes now on the horizon globally, it is crucial clinicians are familiar with the evaluation and management of solitary pulmonary nodules. Through the evaluation of patient risk factors combined with computed tomography characteristics of solitary pulmonary nodules, including size, growth rate, margin characteristics, calcification, density and location; a clinician can assess the risk of malignancy. This article provides an up to date review of the imaging features of both benign and malignant solitary pulmonary nodules to assist in the identification of nodules that require histological confirmation or ongoing surveillance. In addition, we summarise the newly updated Fleischner Society Guidelines that provide clinicians with a framework for the evaluation and management of solitary pulmonary nodules.

Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations

BACKGROUND

The computed tomography (CT) features of small solid lung cancers and their changing regularity as they grow have not been well studied. The purpose of this study was to analyze the CT features of solid lung cancerous nodules (SLCNs) with different sizes and their variations.

METHODS

Between February 2013 and April 2018, a consecutive cohort of 224 patients (225 nodules) with confirmed primary SLCNs was enrolled. The nodules were divided into four groups based on tumor diameter (A: diameter ≤ 1.0 cm, 35 lesions; B: 1.0 cm < diameter ≤ 1.5 cm, 60 lesions; C: 1.5 cm < diameter ≤ 2.0 cm, 63 lesions; and D: 2.0 cm < diameter ≤ 3.0 cm, 67 lesions). CT features of nodules within each group were summarized and compared.

RESULTS

Most nodules in different groups were located in upper lobes (groups A - D:50.8%-73.1%) and had a gap from the pleura (groups A - D:89.6%-100%). The main CT features of smaller (diameter ≤ 1 cm) and larger (diameter > 1 cm) nodules were significantly different. As nodule diameter increased, more lesions showed a regular shape, homogeneous density, clear but coarse tumor-lung interface, lobulation, spiculation, spinous protuberance, vascular convergence, pleural retraction, bronchial truncation, and beam-shaped opacity (p < 0.05 for all). The presence of halo sign in all groups was similar (17.5%-22.5%; p > 0.05).

CONCLUSIONS

The CT features vary among SLCNs with different sizes. Understanding their changing regularity is helpful for identifying smaller suspicious malignant nodules and early determining their nature in follow-up.

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

Background

The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures.

Objectives

The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images.

Methods

A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules. SINRSB CT scans were performed using the same noncontrast enhanced CT protocol and equipment; the malignant nodule data were acquired from several databases. The kurtosis (KUR) and skewness (SKW) values of these tests were determined for the whole volume of each nodule, and the histograms were classified into two basic patterns: peaks or plateaus.

Results

The mean (MEN) KUR values of the SINRSBs and malignant nodules were 3.37 ± 3.88 and 5.88 ± 5.11, respectively. The receiver operating characteristic (ROC) curve showed that the sensitivity and specificity for distinguishing SINRSBs from malignant nodules were 65% and 66% for KUR values >6, respectively, with an area under the curve (AUC) of 0.709 (p < 0.0001). The MEN SKW values of the SINRSBs and malignant nodules were 1.73 ± 0.94 and 2.07 ± 1.01, respectively. The ROC curve showed that the sensitivity and specificity for distinguishing malignant nodules from SINRSBs were 65% and 66% for SKW values >3.1, respectively, with an AUC of 0.709 (p < 0.0001). An analysis of the peak and plateau histograms revealed sensitivity, specificity, and accuracy values of 84%, 74%, and 79%, respectively.

Conclusions

KUR, SKW, and histogram shape can help to noninvasively diagnose SINRSBs but should not be used alone or without considering clinical data.

The Value of 18F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules

Purpose

To establish an ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs).

Materials and Methods

We retrospectively reviewed 177 consecutive patients who underwent ¹⁸F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model.

Results

The mathematical model was y = exp⁡(x)/[1 + exp⁡(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUV_max). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05).

Conclusion

The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs.

Development and validation of a novel diagnostic nomogram model based on tumor markers for assessing cancer risk of pulmonary lesions: A multicenter study in Chinese population

PURPOSE

This study aimed to build a valid diagnostic nomogram for assessing the cancer risk of the pulmonary lesions identified by chest CT.

PATIENTS AND METHODS

A total of 691 patients with pulmonary lesions were recruited from three centers in China. The cut-off value for each tumor marker was confirmed by minimum P value method with 1000 bootstrap replications. The nomogram was based on the predictive factors identified by univariate and multivariate analysis. The predictive performance of the nomogram was measured by concordance index and calibrated with 1000 bootstrap samples to decrease the overfit bias. We also evaluated the net benefit of the nomogram via decision curve analysis. Finally, the nomogram was validated externally using a separate cohort of 305 patients enrolled from two additional institutions.

RESULTS

The cut-off for CEA, SCC, CYFRA21-1, pro-GRP, and HE4 was 4.8 ng/mL, 1.66 ng/mL, 1.83 ng/mL, 56.55 pg/mL, and 63.24Lpmol/L, respectively. Multivariate logistic regression model (LRM) identified tumor size, CEA, SCC, CYFRA21-1, pro-GRP, and HE4 as independent risk factors for lung cancer. The nomogram based on LRM coefficients showed concordance index of 0.901 (95% CI: 0.842-0.960; P < 0.001) for lung cancer in the training set and 0.713 (95% CI: 0.599-0.827; P < 0.001) in the validation set. Decision curve analysis reported a net benefit of 87.6% at 80% threshold probability superior to the baseline model.

CONCLUSION

Our diagnostic nomogram provides a useful tool for assessing the cancer risk of pulmonary lesions identified in CT screening test.

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Purpose of review

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice.

Recent findings

Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening.

Summary

Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Relationship between computed tomography morphology and prognosis of patients with stage I non-small cell lung cancer

This study aimed to explore the relationship between computed tomography morphology and prognosis of patients with stage I non-small cell lung cancer (NSCLC). From May 2009 to May 2011, a total of 124 patients diagnosed with stage I NSCLC were included. All patients had complete chest computed tomography scans. Five-year follow-up was conducted. Univariate and multivariate Cox regression analyses were performed to estimate the prognostic factors for patients with stage I NSCLC. The 5-year survival rate was 67.74% (84/124). The 5-year survival rates of patients with stage T1a, T1b, and T2a were 89.19%, 75.00%, and 41.86%, respectively. The 5-year survival rates of patients with homogeneity, inhomogeneity, vacuole, and cavity were 68.42%, 72.09%, 59.46%, and 83.33%, respectively. The 5-year survival rates of patients with different margin features were 83.33% (slick margin), 79.73% (lobulation sign), and 39.47% (short burr). The 5-year survival rates of patients with normal, halo, vessel convergence, bronchial transection, and vascular bundle thickening were 84.38%, 72.73%, 71.79%, 52.00%, and 47.06%, respectively. The 5-year survival rates of patients with normal and pleura thickening/indentation were 81.93% and 39.02%. Univariate analysis demonstrated that tumor node metastasis staging, tumor margin, tumor periphery, and pleural invasion were related to the prognosis of stage I NSCLC patients. Cox regression analysis confirmed that T2a stage, pleura thickening/indentation were independent risk factors for poor prognosis of stage I NSCLC. In conclusion, our findings indicate that T2a stage, pleura thickening/indentation might be prognostic factors in stage I NSCLC.

Rapid Diagnosis of Lung Tumors, a Feasability Study Using Maldi-Tof Mass Spectrometry

OBJECTIVE

Despite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue.

METHODS

Fresh sample preparations were analyzed with the Microflex LTTM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated.

RESULTS

127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively.

CONCLUSIONS

The feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.